The application of self-organizing network (SON) is studied. The SON automatically optimizes a control parameter of each apparatus in a mobile communication network. Long term evolution (LTE) that is standardized in a third generation partnership project (3GPP) is available as a standard of the mobile communication network where the application of the SON is studied. A variety of use cases of the SON to optimize the control parameter has been under study.
An optimization algorithm called reinforcement learning is known as a type of machine learning. In the reinforcement learning, an agent learns from an interaction with an environment by referencing a state variable of the environment, and improves a policy so that a total amount of reward finally gained is maximized.
A method to control an operation of a first digital subscriber line (DSL) including at least one DSL line is known in the related art. This method includes providing a control DSL line set including at least one DSL line, causing the control DSL line set to operate using a first value in a control parameter vector in a subset of the control DSL line set, collecting operating data related to the operation of the control DSL line set where the first value of the control parameter vector in the subset of the control DSL line set is used, analyzing the collected operating data, and adjusting a value of at least one control parameter of at least one line of the first DSL line set. The analyzing of the collected operating data includes classifying the collected operating data into a plurality of clusters, evaluating a performance measurement criterion in each of the clusters, selecting a first cluster in accordance with the performance measurement criterion of the first cluster, and updating the value of the control parameter vector of the control DSL line set so that the value of the control parameter vector matches the value of the control parameter vector of the selected cluster.
An electric field intensity estimation device that estimates a reception electric field intensity at a given point is available in the related art. The electric field intensity estimation device includes a preliminary estimation unit that determines a pre-correction electric field intensity estimated value from a geographical value indicating a geographical calculation condition at the given point, and a neural network processor that calculates an output value of a neural network having an input layer, at least one intermediate layer, and an output layer, and updates weighting coefficients. In an estimation mode, the geographical value is used as an input value to the input layer, a correction value is calculated in accordance with an output value output from the output layer, and the correction value and the electric field intensity prior to correction are summed. The electric field intensity estimation device thus calculates and outputs a corrected electric field intensity estimation value. In a learning mode, the electric field intensity estimation device sets, as an output value of the output layer, an error between a correction value calculated in the estimation mode and a target correction value. The target correction value is a difference between the pre-correction electric field intensity estimation value and an actual measurement value. The neural network processor updates the weighting coefficients through backpropagation.
Also available in the related art is a characteristic pattern detection system that includes a model learning apparatus that learns a model in a sensor network as a network having a sensor, and a characteristic pattern detection apparatus that acquires a measurement value characteristic of the senor network. The model learning apparatus receives, from a measurement value database, data of the measurement value of each sensor installed in the sensor network, determines a parent sensor group from the measurement data and a prior knowledge relating to a sensor retrieved from a prior knowledge database, determines a statistical parameter of a sensor measurement value based on a reliance relationship between the determined sensor and the parent sensor group, and then transmits the statistical parameter to the characteristic pattern detection apparatus. The characteristic pattern detection apparatus receives from the measurement value database a measurement pattern that is measurement data at a measurement time serving as a characteristic pattern detection target, and determines a characteristic of the received measurement pattern using information relating to the parent sensor group and the statistical parameter to detect a fault.